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Urban Flood Detection with Sentinel-1 Multi-Temporal Synthetic Aperture Radar (SAR) Observations in a Bayesian Framework: A Case Study for Hurricane Matthew

1
Institute of Earth Sciences, Academia Sinica, Taipei City 115, Taiwan
2
Earth Observatory of Singapore, Nanyang Technological University, Singapore 639798, Singapore
3
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
4
Asian School of the Environment, Nanyang Technological University, Singapore 637459, Singapore
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(15), 1778; https://doi.org/10.3390/rs11151778
Received: 4 June 2019 / Revised: 23 July 2019 / Accepted: 24 July 2019 / Published: 29 July 2019
(This article belongs to the Special Issue Imaging Floods and Glacier Geohazards with Remote Sensing)
In this study we explored the application of synthetic aperture radar (SAR) intensity time series for urban flood detection. Our test case was the flood in Lumberton, North Carolina, USA, caused by the landfall of Hurricane Matthew on 8 October 2016, for which airborne imagery—taken on the same day as the SAR overpass—is available for validation of our technique. To map the flood, we first carried out normalization of the SAR intensity observations, based on the statistics from the time series, and then construct a Bayesian probability function for intensity decrease (due to specular reflection of the signal) and intensity increase (due to double bounce) cases separately. We then formed a flood probability map, which we used to create our preferred flood extent map using a global cutoff probability of 0.5. Our flood map in the urban area showed a complicated mosaicking pattern of pixels showing SAR intensity decrease, pixels showing intensity increase, and pixels without significant intensity changes. Our approach shows improved performance when compared with global thresholding on log intensity ratios, as the time series-based normalization has accounted for a certain level of spatial variation by considering the different history for each pixel. This resulted in improved performance for urban and vegetated regions. We identified smooth surfaces, like asphalt roads, and SAR shadows as the major sources of underprediction, and aquatic plants and soil moisture changes were the major sources of overprediction. View Full-Text
Keywords: SAR intensity time series; urban flood mapping; double bounce effect; Hurricane Matthew SAR intensity time series; urban flood mapping; double bounce effect; Hurricane Matthew
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Lin, Y.N.; Yun, S.-H.; Bhardwaj, A.; Hill, E.M. Urban Flood Detection with Sentinel-1 Multi-Temporal Synthetic Aperture Radar (SAR) Observations in a Bayesian Framework: A Case Study for Hurricane Matthew. Remote Sens. 2019, 11, 1778.

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